I already heard that FastText is generating OOV word vectors using its n-gram's. It is already automatically built-in at FastText architecture or we should like to tune specific parameters to it? like an oov_tokens in Keras tokenizer. I already looking for what parameters to tune in Fast Text but I couldn't find any.
If anyone knows and wants to share their knowledge I would be very appreciative of that.
Thank you.
Vector generation for OOV words is integrated into fastText (at least in the original implementation by Facebook).
To generate these vectors, fastText uses subword n-grams. To learn more, you can read this thread and this visual guide.
For this reason, the parameters that most influence the creation of vectors for OOV words are the following:
minn (min length of char ngram)maxn (max length of char ngram)For more information about fastText options/parameters, see the official documentation.